Posts Tagged ‘ROI’
The Problem of Measurability
Along my meandering path, I had many opportunities to interact with brands and retailers. You’ll also notice that word-of-mouth played heavily into my decision. And this is not just anecdotal – there is a lot of research to support the fact that most purchasing decisions are heavily influenced by word-of-mouth. Also, my cartoon illustrates the interactions I remember – but there are many brand and business interactions I don’t remember. For example, several local retailers are very active in the local running community, including online forums. I tend to think of those businesses as authoritative, even if they are not speaking directly to my current interest. There are also race and athlete sponsorships that create a positive association with brands, even if I don’t consciously remember them.
The lesson here is that much of what happened prior to my purchase was not directly measurable. In fact, if someone were measuring, they would probably think that search advertising or organic ranking accounted for my purchase, but I had all but made my decision at that point. Online reporting tools are good at measuring search clicks, but not so good at measuring everything else that happened prior to that last search.
There are some reporting tools that do a better job than others of measuring all of the interactions that lead to a conversion. See some of the great research done by the Atlas Institute for more on this topic. (Disclosure: I used to work for Atlas, but the research is still great.) I’ve found both Atlas and Omniture Discover to be very useful when trying to understand buyer behavior, but both of these are too expensive for small businesses. Unfortunately, Google Analytics does a poor job at this even though it is a very powerful tool in many respects.
The solution for small business lies in combining online conversion data with other on- and offline sources, such as a Facebook Insights reports and “how did you hear about us” questionnaires. Also, engagement metrics such as time-on-site and bounce rate can be very useful indicators. At Two Octobers, we produce our own dashboard reports that draw from multiple sources to provide a more complete and accurate view. We pull it all together to show how all online activities and channels are contributing to business goals. There is no exact formula – the right combination of data sources and indicators depends on your business model and marketing methods.
If you know of other useful tools and techniques, please comment below.
Paid Search: Bidding with Confidence
In the previous article, Paid Search: Bidding Based on ROI, we showed how you can use ROI data to determine optimal bids. The methods we described assume that you know the conversion rate of a keyword, ad group or campaign. But in fact you can’t ever really know a conversion rate, the best you can do is to estimate using historical data. This article explains how to use confidence intervals to ensure that your estimates are reasonably accurate.
For example, let’s say that a keyword has had 100 clicks and 2 conversions. Do you know what the conversion rate is for that keyword? The obvious answer is 2%, but the correct answer is “no”. From experience, you probably know that you could get fewer or more conversions in the next 100 clicks. Given a set of sample data, the best you can say is that you expect that the conversion rate falls between X% and Y%. In statistics, this is called a “confidence interval”. A confidence interval also has a “confidence level”. The confidence level describes how sure you are that the conversion rate falls between X% and Y%. For example, an 80% confidence level means you can be 80% sure that the actual value falls between the lower and upper bound of the interval. This means that there is a 10% chance that the actual value falls below x% and a 10% chance that it falls above Y%.
Below is a table of confidence intervals, with the column on the left indicating the number of conversions observed, and the row across the top indicating the number of clicks observed. The confidence level for these intervals has been set at 80%. The cells with white backgrounds show the confidence interval for each combination of conversions and clicks. The confidence intervals are expressed as X% – Y%, with X% being the lower bound, and Y% being the upper bound.
| 100 | 200 | 300 | 400 | 500 | |
| 0 | 0 % – 2.3% | 0% – 1.1% | 0% – 0.7% | 0% – 0.6% | 0% – 0.5% |
| 1 | 0.1% – 3.8% | 0.1% – 1.9% | 0% – 1.3% | 0% – 1% | 0% – 0.8% |
| 2 | 0.5% – 5.2% | 0.3% – 2.6% | 0.2% – 1.8% | 0.1% – 1.3% | 0.1% – 1.1% |
| 3 | 1.1% – 6.6% | 0.6% – 3.3% | 0.4% – 2.2% | 0.3% – 1.7% | 0.2% – 1.3% |
| 4 | 1.8% – 7.8% | 0.9% – 4% | 0.6% – 2.7% | 0.4% – 2% | 0.4% – 1.6% |
| 5 | 2.5% – 9.1% | 1.2% – 4.6% | 0.8% – 3.1% | 0.6% – 2.3% | 0.5% – 1.9% |
| 6 | 3.2% – 10.3% | 1.6% – 5.2% | 1.1% – 3.5% | 0.8% – 2.6% | 0.6% – 2.1% |
For example, if we observe 3 conversions over 400 clicks, the confidence interval ranges from 0.3% to 1.7% – I have shaded that cell pink in the table. Therefore, we are 80% sure that the actual conversion rate falls between these two values. Compare the ranges in the “500” column to the “100” column and you will notice an important fact about confidence intervals: the more data we have, the smaller the confidence interval. And the smaller the confidence interval, the better an idea we have of the actual value. But also note that there is always an interval – we never truly know the actual value from observed data. Here are a few scenarios demonstrating how this data might be applied:
- Jane buys 200 clicks and gets no conversions and decides that paid search is a waste of money. Lisa points out that there’s a good chance that Jane’s conversion rate is as much as 1% and that she should wait it out a bit.
- Lisa wants to bid based on a target CPA. After buying 300 clicks, she gets 5 leads. He assumes a conversion rate of 0.8%. Given that the confidence interval is 0.8%-3.1%, Lisa is being conservative by taking the lower bound of the range.
- Jane thinks her conversion rate is 10%, but she measures 4 conversions over 200 clicks for her AdWords campaign. She realizes the data is not supporting her assumption: based on the data she can be 90% confident that her conversion rate is not more than 4%.
The main takeaway from this table is that even 500 clicks is not enough to have a very accurate idea of conversion rate, which is why I say you shouldn’t be bidding on most individual keywords based on ROI goals–there generally isn’t enough keyword-level data to make good decisions.
If you want to test out some confidence intervals of your own, this site has an interactive calculator. Use the binomial confidence interval and change the confidence level at the bottom of the page if you want it to be different than 95%. The higher the confidence level, the larger the intervals you will get. The numerator (x) is the number of conversions you observe, and the denominator (N) is the number of clicks you bought.
The approach I like to take when bidding is to start with an idea of the conversion rate for the campaign as a whole, and set bids based on that, since I have the most data at the campaign level. Then I look at individual ad groups and change bids if the confidence interval tells me that the ad group conversion rate is likely more or less than the campaign conversion rate. Then I look at individual keywords, but only those that have enough data to produce a useful confidence interval.
At Two Octobers we use automated tools to manage bid setting according to these principals, but it’s important to understand the underlying concepts. And bids are just one lever in a campaign. Ad copy, keyword selection and the user experience are at least as important as tweaking bids. More on that to come.
This article gives a high-level overview of a very complex topic. I hope it is useful to you, but please contact us if you would like help optimizing your paid search campaigns.
Paid Search: Bidding Based on ROI
This article explains some basic concepts related to bidding on paid search keywords based on return on investment (ROI). Using ROI to make bidding decisions helps ensure that you are spending your advertising dollars in the most effective ways possible.
To explain these concepts, we will use paid search marketers Jane and Lisa as examples.
- Jane’s goal is to generate as much revenue as possible, but she doesn’t want to spend more than a dollar in advertising for every $5 in sales.
- Lisa’s goal is to drive as many leads possible at a cost per acquisition (CPA) of $10.
A few definitions to start things off:
- Click – a paid search click bought on Google, Yahoo or Bing
- Conversion – a click-to-sale conversion. A conversion is when someone clicks through to your site, then completes a purchase. This could also be a click-to-lead conversion if your goal is to drive leads
- Conversion Rate – the percentage of time clicks convert to sales. For example, a conversion rate of 5% means that 5 out of every 100 clicks result in sales.
- Average Sale – the average value of a sale
- ROAS – return on ad spend: the return in sales you are getting on your paid search investment. I will calculate this as $X in sales for every dollar spent
- CPC – cost per click: what you are paying per click in Google, Yahoo or Bing
- Target CPC – the cost per click you should be paying to achieve your target ROAS
Here’s an example scenario: Jane is paying $0.10 per click, 10% of clicks are converting to sales, and her average sale is $5. This means that on average she generates $5 in revenue for every $1 she spends to buy clicks. Therefore, her ROAS is $5.
If Jane is ok with a lower ROAS, she can generate more sales. Spending more per click will lower her ROAS, but the higher CPC will also drive more traffic. For example, if she can achieve her profit goals at a $2.50 ROAS, she can afford to pay $0.20 per click. Here is the math:
ROAS = Sales / Ad Spend
= (Average Sale X Conversion Rate X Clicks) / (Clicks X CPC)
= (Average Sale X Conversion Rate) / CPC
Solving for CPC, we get:
Target CPC = (Average Sale X Conversion Rate) / Target ROAS
Substituting Jane’s goal for ROAS, we get:
Target CPC = ($5 X 10% / $2.50) = $0.20
The process is similar for Lisa. Cost per acquisition is total cost divided by the number of acquisitions, or:
CPA = Ad Spend / Conversions
= (Clicks X CPC) / (Clicks X Conversion Rate)
= CPC / Conversion Rate
Solving for CPC, we get:
Target CPC = Target CPA X Conversion Rate
Substituting Lisa’s goal for CPA and assuming a conversion rate of 10%, we get:
Target CPC = $10 X 10% = $1.00
These calculations are pretty straightforward, and many paid search marketers use some variation of these methods. There are also a number of automated bidding systems that have formulas like these somewhere under the hood.
One assumption we’ve made is that we know Jane and Lisa’s conversion rates, but that is not necessarily a safe assumption. To learn more about the challenges of estimating conversion rates, have a look at this article: Paid Search: Bidding with Confidence.
And if you do not currently have the ability to track leads or sales to a keyword ad, Google Analytics is free and will do the job. If you would like help tracking performance or optimizing your paid search campaigns, have a look at our services, or contact us.
